arXiv:2606.04401v1 Announce Type: new Abstract: The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains. Optimizing domain-specific mixture ratios can be modeled as a bi-level optimization problem, which we simplify into a single-level penalized form and solve with twin networks: a proxy model trained on primary data and a dynamically updated reference model trained with additional data. Our proposed method, Twin Networks for bi-level DatA mixturE optiMization (TANDEM), measures the data efficacy through the difference between the twin
The proliferation of various domain-specific datasets for large language model (LLM) training necessitates more sophisticated methods for data mixture optimization, which this research addresses.
Efficient data mixture optimization is critical for maximizing LLM capabilities, directly impacting model performance, training costs, and the effective utilization of available data resources.
The proposed TANDEM method offers a new approach to bi-level optimization for data mixing, potentially leading to more effective and resource-efficient LLM training strategies.
- · LLM developers
- · Data scientists
- · AI research institutions
- · Cloud computing providers
- · LLMs trained with suboptimal data mixtures
- · Manual data curation processes
Improved performance and reduced training costs for large language models.
Faster development cycles for specialized AI applications due to more effective use of specific datasets.
Enhanced accessibility for smaller organizations to develop competitive LLMs by optimizing their limited data resources.
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Read at arXiv cs.LG